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Write SQL to monitor weekly chargeback spikes

StripeData Scientist · Onsite

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This question evaluates proficiency in PostgreSQL time-series analytics, specifically aggregations, joins across payments and chargebacks, handling late-arriving events, and calculating per-week metrics such as GMV, active merchants, and chargeback rates.

Write a single SQL query (PostgreSQL) to detect weekly chargeback spikes by country and industry. Week starts Monday (use date_trunc('week', ts) with ISO weeks). Schema and sample data:

Tables

  • merchants(merchant_id INT, country_code TEXT, industry TEXT)
  • payments(payment_id INT, merchant_id INT, created_at TIMESTAMP, amount_usd NUMERIC, status TEXT CHECK (status IN ('succeeded','failed')))
  • chargebacks(chargeback_id INT, payment_id INT, created_at TIMESTAMP)

Sample rows merchants +-------------+--------------+----------+ | merchant_id | country_code | industry | +-------------+--------------+----------+ | 1 | US | SaaS | | 2 | US | Retail | | 3 | DE | Retail | | 4 | US | SaaS | +-------------+--------------+----------+

payments +------------+-------------+---------------------+------------+-----------+ | payment_id | merchant_id | created_at | amount_usd | status | +------------+-------------+---------------------+------------+-----------+ | 101 | 1 | 2025-08-18 10:00:00 | 120.00 | succeeded | | 102 | 1 | 2025-08-19 11:00:00 | 130.00 | succeeded | | 103 | 2 | 2025-08-26 09:00:00 | 80.00 | failed | | 104 | 2 | 2025-08-26 12:00:00 | 200.00 | succeeded | | 105 | 3 | 2025-08-27 13:00:00 | 75.00 | succeeded | | 106 | 4 | 2025-09-02 10:00:00 | 500.00 | succeeded | | 107 | 1 | 2025-09-02 12:00:00 | 150.00 | succeeded | +------------+-------------+---------------------+------------+-----------+

chargebacks +----------------+------------+---------------------+ | chargeback_id | payment_id | created_at | +----------------+------------+---------------------+ | 9001 | 101 | 2025-09-01 08:00:00 | | 9002 | 104 | 2025-09-08 09:00:00 | +----------------+------------+---------------------+

Task Return, for each (week_start, country_code, industry):

  • gmv_usd: sum(amount_usd) of succeeded payments created in that week
  • active_merchants: distinct merchants with ≥1 succeeded payment that week
  • succeeded_cnt: count of succeeded payments that week
  • cb_cnt_lagwin: chargebacks created during that week whose underlying payment was created in the lag window [week_start-14 days, week_start-7 days]
  • cb_rate_lagwin: cb_cnt_lagwin / succeeded_cnt for payments in that lag window
  • wow_cb_rate_pct_increase: week-over-week percentage change of cb_rate_lagwin
  • top_merchant_share_gmv: in-week max(merchant_gmv)/gmv_usd Filter to rows where wow_cb_rate_pct_increase > 50% AND top_merchant_share_gmv < 0.40. Handle late-arriving chargebacks by joining chargebacks to payments via payment_id. Assume UTC throughout. Use CTEs; no temp tables. Also, based on the sample data, state which (week_start, country_code, industry) rows should be returned and why (briefly).